Closed Loop Blood Glucose Control Algorithm Analysis

ABSTRACT

Methods and devices to generate a tool for testing, simulating and/or modifying a closed loop control algorithm are provided. Embodiments include receiving glucose data for a predetermined time period, determining a variation in the glucose level based on the received glucose data, filtering a received glucose data based on the determined variation, substituting a negative change in the glucose data value with a predetermined value to generate a sequence of modified glucose values, and integrating the sequence of modified glucose values to determine an uncontrolled blood glucose excursion condition.

RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 12/769,634 filed on Apr. 28, 2010, now U.S. Pat. No. 8,467,972,which claims priority under 35 U.S.C. §119(e) to U.S. provisionalapplication No. 61/173,598 filed Apr. 28, 2009, entitled “Closed LoopBlood Glucose Control Algorithm Analysis”, the disclosures of each ofwhich are incorporated in its entirety by reference for all purposes.

BACKGROUND

A desirable diabetes management and treatment includes a combinedcontinuous blood glucose monitoring and insulin delivery system thatoperate autonomously. In such systems, control software would monitorthe output of the continuous blood glucose monitor and calculateappropriate delivery instructions for the insulin delivery system. Sucha system is often referred to as closed loop blood glucose control andthe control software is often referred to as a closed loop blood glucosecontrol algorithm.

The development of a closed loop blood glucose control algorithm is themost challenging aspect of the development of a closed loop bloodglucose control system. This challenge arises from complicated featuresof diabetes management such as noise and delays that are inherentfeatures of blood glucose monitoring and insulin delivery. Anothercomplicating aspect of developing a closed loop blood glucose controlalgorithm is that individuals can differ significantly in the details oftheir lifestyle (e.g., diet, activity level) and in the details of theirphysiology (e.g., size, fitness, insulin sensitivity).

Furthermore, failure of a closed loop blood glucose control algorithmcould potentially have lethal consequences. Thus a closed loop bloodglucose control algorithm will need to be comprehensively tested andlikely need to be tuned or personalized to each individual user.

Currently, testing a closed loop blood glucose control algorithmrequires the use of a living diabetic subject or a mathematical model ofa diabetic subject. The living subject can be animal or human. Testing aclosed loop blood glucose control algorithm on a living subject suffersfrom the disadvantage that such testing is expensive, time consuming andposes significant risks to the health of the test subject. Testing aclosed loop blood glucose control algorithm with a mathematical model ofa diabetic subject suffers from the fact that human physiology is fartoo complex to be sufficiently represented by any currently availablemathematical model. The main advantage of the method described herein isthat it is fast, inexpensive and incurs no risk and also captures theinherent complexity of a live diabetic subject.

SUMMARY

Embodiments of the subject disclosure include device and methodscomprising receiving glucose data for a predetermined time period,determining a variation in the glucose level based on the receivedglucose data, filtering a received glucose data based on the determinedvariation, substituting a negative change in the glucose data value witha predetermined value to generate a sequence of modified glucose values,and integrating the sequence of modified glucose values to determine anuncontrolled blood glucose excursion condition.

An apparatus in a further aspect includes a user interface, one or moreprocessors operatively coupled to the data user interface, and a memoryfor storing instructions which, when executed by the one or moreprocessors, causes the one or more processors to receive glucose datafor a predetermined time period, determine a variation in the glucoselevel based on the received glucose data, filter a received glucose databased on the determined variation, substitute a negative change in theglucose data value with a predetermined value to generate a sequence ofmodified glucose values, and integrating the sequence of modifiedglucose values to determine an uncontrolled blood glucose excursioncondition.

In still another aspect, one or more storage devices having processorreadable code embodied thereon, the processor readable code forprogramming one or more processors to perform a control test algorithmcomprising receiving glucose data for a predetermined time period,determining a variation in the glucose level based on the receivedglucose data, filtering a received glucose data based on the determinedvariation, substituting a negative change in the glucose data value witha predetermined value to generate a sequence of modified glucose values,and integrating the sequence of modified glucose values to determine anuncontrolled blood glucose excursion condition.

Also provided are systems, computer program products, and kits.

INCORPORATION BY REFERENCE

The following patents, applications and/or publications are incorporatedherein by reference for all purposes: U.S. Pat. Nos. 4,545,382;4,711,245; 5,262,035; 5,262,305; 5,264,104; 5,320,715; 5,509,410;5,543,326; 5,593,852; 5,601,435; 5,628,890; 5,820,551; 5,822,715;5,899,855; 5,918,603; 6,071,391; 6,103,033; 6,120,676; 6,121,009;6,134,461; 6,143,164; 6,144,837; 6,161,095; 6,175,752; 6,270,455;6,284,478; 6,299,757; 6,338,790; 6,377,894; 6,461,496; 6,503,381;6,514,460; 6,514,718; 6,540,891; 6,560,471; 6,579,690; 6,591,125;6,592,745; 6,600,997; 6,605,200; 6,605,201; 6,616,819; 6,618,934;6,650,471; 6,654,625; 6,676,816; 6,730,200; 6,736,957; 6,746,582;6,749,740; 6,764,581; 6,773,671; 6,881,551; 6,893,545; 6,932,892;6,932,894; 6,942,518; 7,167,818; and 7,299,082; U.S. PublishedApplication Nos. 2004/0186365; 2005/0182306; 2007/0056858; 2007/0068807;2007/0227911; 2007/0233013; 2008/0081977; 2008/0161666; and2009/0054748; U.S. patent application Ser. Nos. 11/831,866; 11/831,881;11/831,895; 12/102,839; 12/102,844; 12/102,847; 12/102,855; 12/102,856;12/152,636; 12/152,648; 12/152,650; 12/152,652; 12/152,657; 12/152,662;12/152,670; 12/152,673; 12/363,712; 12/131,012; 12/242,823; 12/363,712;12/393,921; 12/495,709; 12/698,124; 12/699,653; 12/699,844; 12/714,439;12/761,372; and 12/761,387 and U.S. Provisional Application Ser. Nos.61/230,686 and 61/227,967.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a graphical illustration of blood glucose data using acontinuous glucose monitoring system in accordance with aspects of thepresent disclosure;

FIG. 2 is a graphical illustration of filtered minute to minute changesin blood glucose data from the data set shown in FIG. 1 in aspects ofthe present disclosure;

FIG. 3 is a graphical illustration of filtered minute to minute changesin blood glucose data from the data set shown in FIG. 1 in aspects ofthe present disclosure;

FIG. 4 is a graphical illustration of an uncontrolled blood glucoseexcursion generated from the data in FIG. 3 in aspects of the presentdisclosure;

FIG. 5 is a graphical illustration of an example of output from a closedloop control simulation in accordance with embodiments of the presentdisclosure; and

FIG. 6 is a block diagram illustrating an overall system for executingclosed loop control simulation routines in accordance with embodimentsof the present disclosure.

DETAILED DESCRIPTION

Before the present disclosure is described in additional detail, it isto be understood that this disclosure is not limited to particularembodiments described, as such may, of course, vary. It is also to beunderstood that the terminology used herein is for the purpose ofdescribing particular embodiments only, and is not intended to belimiting, since the scope of the present disclosure will be limited onlyby the appended claims.

Where a range of values is provided, it is understood that eachintervening value, to the tenth of the unit of the lower limit unlessthe context clearly dictates otherwise, between the upper and lowerlimit of that range and any other stated or intervening value in thatstated range, is encompassed within the disclosure. That the upper andlower limits of these smaller ranges may independently be included inthe smaller ranges is also encompassed within the disclosure, subject toany specifically excluded limit in the stated range. Where the statedrange includes one or both of the limits, ranges excluding either orboth of those included limits are also included in the disclosure.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this disclosure belongs. Although any methods andmaterials similar or equivalent to those described herein can also beused in the practice or testing of the present disclosure, the preferredmethods and materials are now described. All publications mentionedherein are incorporated herein by reference to disclose and describe themethods and/or materials in connection with which the publications arecited.

It must be noted that as used herein and in the appended claims, thesingular forms “a”, “an”, and “the” include plural referents unless thecontext clearly dictates otherwise.

The publications discussed herein are provided solely for theirdisclosure prior to the filing date of the present application. Nothingherein is to be construed as an admission that the present disclosure isnot entitled to antedate such publication by virtue of prior disclosure.Further, the dates of publication provided may be different from theactual publication dates which may need to be independently confirmed.

As will be apparent to those of skill in the art upon reading thisdisclosure, each of the individual embodiments described and illustratedherein has discrete components and features which may be readilyseparated from or combined with the features of any of the other severalembodiments without departing from the scope or spirit of the presentdisclosure.

The figures shown herein are not necessarily drawn to scale, with somecomponents and features being exaggerated for clarity.

Generally, embodiments of the present disclosure are directed todeveloping and testing a closed loop blood glucose control algorithm.The embodiments disclosed herein use continuous blood glucose data toconstruct a realistic test input that may be used as an aid indeveloping, testing or tuning a closed loop blood glucose controlalgorithm. In one aspect, the algorithm analyzes a set of continuousblood glucose data and processes it to generate a hypotheticaluncontrolled blood glucose excursion. This uncontrolled blood glucoseexcursion can then be used as a test input to aid in developing, testingor tuning a closed loop blood glucose control algorithm. In one aspect,the approach described in accordance with the various embodiments allowsa closed loop blood glucose control algorithm to be custom tailored tothe unique requirements of a diabetic individual.

In one aspect, analysis may be performed on continuously monitoredglucose data to generate a tool that may be used to develop, test ortune a closed loop blood glucose control algorithm. A set of continuousblood glucose data shows the increases and decreases in blood glucosecorresponding to various metabolic processes that add glucose to orremove glucose from the body. For a diabetic person with poor bloodglucose control, these increases and decreases in blood glucose can bevery distinct because consumption of carbohydrates and injection ofinsulin are not well matched.

FIG. 1 shows a plot of glucose data generated using a continuous glucosemonitoring system such as, for example, Freestyle Navigator® ContinuousGlucose Monitoring System available from Abbott Diabetes Care Inc.,Alameda Calif. It can be seen that the continuous glucose data shownFIG. 1 includes data from a diabetic subject with poor glucose controlover a 24 hour period with data points obtained every one minute. As canbe further seen from FIG. 1, the overall data set is composed ofdistinct subsets of data in which blood glucose is continuouslyincreasing (upslopes shown in FIG. 1) and distinct subsets of data inwhich blood glucose is continuously decreasing (downslopes shown in FIG.1).

Additional detailed descriptions of embodiments of the analytemonitoring system, embodiments of its various components are provided inU.S. Pat. Nos. 5,262,035; 5,264,104; 5,262,305; 5,320,715; 5,593,852;6,103,033; 6,134,461; 6,175,752; 6,560,471; 6,579,690; 6,605,200;6,654,625; 6,746,582; and 6,932,894; and in U.S. Published PatentApplication No. 2004/0186365, the disclosures of which are hereinincorporated by reference. Furthermore, detailed description of signalprocessing related to sensor initialization, signal filtering, andprocessing in analyte monitoring systems can be found in U.S. Pat. Nos.6,175,751, 6,560,471, and in U.S. patent application Ser. No. 12/152,649filed May 14, 2008, disclosure of each of which are incorporated hereinby reference for all purposes. Additionally, details of closed loopcontrol system with safety parameters are described in U.S. PublishedPatent Application No. U.S. 2009/0105636 filed Aug. 31, 2008, thedisclosure of which is incorporated herein by reference for allpurposes.

As discussed above, this pattern or distinct subset may be an indicationthat for the individual from whom the data set was derived, consumptionof carbohydrates and injection of insulin are not well matched.Accordingly, in one aspect of the present disclosure, the subsets ofdata in which blood glucose is continuously increasing may be separatedfrom the subsets of data in which blood glucose is continuouslydecreasing. The subsets of data in which blood glucose is decreasing arethen removed and replaced with an artificially constructed subset ofblood glucose data that serves as an extrapolation of the subset ofincreasing blood glucose data that preceded it. In one aspect, thesubsets of data in which blood glucose is continuously increasing andthe artificially constructed subset of blood glucose data may beassociated or linked together. This results in a plot of how bloodglucose level increases with time if glucose was never consumed i.e., ahypothetical uncontrolled blood glucose excursion. This hypotheticaluncontrolled blood glucose excursion may then be used as a test input toa closed loop blood glucose control algorithm to aid in developing,testing or tuning that algorithm. This analysis of the blood glucosedata can readily be performed using a conventional software program suchas a spreadsheet program.

In one aspect of the present disclosure, continuously monitored glucosedata such as that shown in FIG. 1 is collected. As the collected dataindicates, for example, shown in FIG. 1, the increase and decrease ofblood glucose value over time may be due to various metabolic processesincluding the influx of glucose from the gut, release and uptake ofglucose from the liver and insulin dependent utilization of glucose bycells in the body. The collected data may be uploaded into a spreadsheetprogram for processing as a time sequential data. For reference, thetime stamp for each data point is associated with each one minute data.The resulting data is then plotted as, for example, shown in FIG. 1.

Using the collected and/or plotted data set, the change in glucose valuefrom one minute to the next is determined. For example, the change inblood glucose value from one minute to the next can be determined. Inone aspect, minute to minute variation in blood glucose level that areunrealistically large may also be filtered out as they are likely due tonoise or signal artifacts in the continuous glucose sensor. For example,it can be seen that a rate of change in blood glucose level greater than2.5 mg/dl per minute is likely to be caused by noise in the continuousblood glucose monitoring system. FIG. 2 illustrates a plot of thefiltered minute to minute changes in blood glucose data based on thedata of FIG. 1.

As a separate set of data, in one aspect, negatively valued changes inblood glucose are filtered out and replaced. In one aspect, differentapproaches or values may be used for replacement values to separate thesubsets of blood glucose data where blood glucose is increasing fromthose subsets of data where blood glucose is decreasing. The subsets ofblood glucose data where blood glucose is increasing are then linked orassociated with an artificially constructed subset of blood glucosedata. The artificially constructed subset of blood glucose data isgenerated so as to effect an extrapolation of the previous subset ofblood glucose data where blood glucose is increasing.

Specifically, in one embodiment, negatively valued changes in bloodglucose may be replaced with a zero value. In this manner, theartificially constructed subset of blood glucose data has the effect ofholding the blood glucose constant between the subsets of increasingblood glucose. An example of how this would be implemented as a logicstatement is shown in equation 1 below where “n” is the specific bloodglucose value in question.

if n<0, then n=0, otherwise n=n  (1)

In an alternative embodiment negatively valued changes in blood glucoseare replaced with a constant positive value. In this manner, theartificially constructed subset of blood glucose data may be generatedso as to affect a constant increase in blood glucose value. An exampleof how this would be implemented as a logic statement is shown inequation 2 below where “n” is the specific blood glucose value inquestion and “x” is the constant rate of blood glucose increase in theartificially constructed subset of blood glucose data.

if n<0, then n=x, otherwise n=n  (2)

In yet a further embodiment, negatively valued changes in blood glucosemay be replaced with an average value of all or part of the previoussubset of increasing blood glucose value. In this manner, the glucosetrend that was present in the previous subset of increasing bloodglucose value may be preserved in the artificially constructed subset ofblood glucose data. The artificially constructed subset of blood glucosedata may extrapolate the trend in blood glucose data that was present inthe previous subset of blood glucose data that was increasing. Anexample of this approach as a logic statement is shown in equation 3below where “n” is the specific blood glucose value in question and “y”is the number of previous positively valued blood glucose values whichare averaged.

if n<0, then n=average previous y positive values, otherwise n=n  (3)

FIG. 3 illustrates a plot of the aforementioned filtered minute tominute changes in blood glucose data. As can be seen, the subsets ofdata where blood glucose level is decreasing are removed by replacingnegatively valued changes in blood glucose with the average of theprevious 10 positively valued changes in blood glucose.

In still a further embodiment, the negatively valued changes in bloodglucose may be replaced with a blood glucose value that is a predefinedfunction of all or part of the previous subset of increasing bloodglucose values. For example, a linear regression curve fit may beapplied to all or part of the previous subset of increasing bloodglucose values. This linear regression curve fit may be used toextrapolate values to replace decreasing blood glucose values.Alternatively, a higher order curve fit may be applied to all or part ofthe previous subset of increasing blood glucose values. This curve fitcan then be used to extrapolate values to replace decreasing bloodglucose values.

After obtaining the filtered analyzed data set as discussed above, theminute to minute positive changes in blood glucose level are integratedinto a continuous uncontrolled blood glucose excursion. In one aspect,integrating the minute to minute positive changes in blood glucose levelinto the continuous uncontrolled blood glucose excursion may be achievedby selecting a blood glucose value to start with, for example, 100mg/dl, and adding each minute's blood glucose change to this initialvalue.

FIG. 4 illustrates a plot of an uncontrolled blood glucose excursiongenerated from the data in FIG. 3. The plot shown in FIG. 4 includes allof the actual subsets of blood glucose data from FIG. 1 where bloodglucose is increasing linked together with artificially constructedsubsets of blood glucose data that are extrapolations of the subsets ofincreasing blood glucose data that preceded them. The uncontrolled bloodglucose excursion in FIG. 4 shows a change in blood glucose of about1400 mg/dl over the course of 24 hrs. For a 70 kg person, this mayresult from the consumption of about 200 grams (1000 calories) ofcarbohydrates which is a reasonable amount for a daily consumption.Moreover, as shown in FIG. 4, the subtle features and irregularities ofblood glucose data that arise as a result of the unique features of anindividual's lifestyle and physiology are illustrated.

In one aspect, the profile illustrated in FIG. 4 is used as a test inputto a closed loop control simulation. FIG. 5 illustrates an example ofoutput from a simple closed loop control simulation. The simulation usesthe data in FIG. 4 as a test input and a PID control algorithm. Thecontrol simulation additionally uses a model for insulin sensitivity andfor insulin pharmacokinetics. Blood glucose is shown on the upper curveand insulin concentration is shown on the lower curve.

The insulin concentration is due to insulin that was administered by thecontroller in response to the blood glucose behavior in FIG. 4. Thevarious values for controller gain can readily be changed to affectoptimal control. Values for insulin sensitivity and insulinpharmacokinetics as well as parameters for the continuous blood glucosemonitor's performance can also be changed to assess the robustness ofthe controller.

FIG. 6 is a block diagram illustrating an overall system for executingclosed loop control simulation routines in accordance with embodimentsof the present disclosure. Referring to FIG. 6, in certain embodiments,data, such as glucose data, for use in the closed loop controlsimulation routines described above, is received at a system 600 via acommunication module 603. The communication module 603 may be a wiredconnection port configured to receive data via a wired connection, suchas, among others, a universal serial bus (USB) connection, RS-232 serialconnection, parallel connection, or Ethernet connection, or may be awireless communication module configured for, among others, radiofrequency (RF) communication protocol, Bluetooth® communicationprotocol, infrared (IR) communication protocol, or 802.11 WiFicommunication protocol. The communication module 603 is coupled to aprocessor 601 or other processing unit. The processor 601, may be, amongothers, a microprocessor, microcontroller, CPU, or an applicationspecific integrated circuit (ASIC). The processor 601 and thecommunication module 603 are also coupled to a memory 602. In certainembodiments, the memory 602 may be integral with the processor 601. Inother embodiments, the memory 602 may be a separate unit external fromthe processor 601 unit and coupled via a communication interface.

Still referring to FIG. 6, the glucose data received at thecommunication module 603 is stored in the memory 602 under control ofthe processor 601. The memory 602 additionally stores programminginstructions for execution by the processor 601 for executing closedloop control simulation routines, such as the closed loop controlsimulation routines described above, based on the glucose data receivedat the communication module 603 and stored in the memory 602. The systemmay further include an output module 604 configured for transmission oroutput of the results of the closed loop control simulation routines. Incertain embodiments the output module 604 transmits the results of theclosed loop control simulation routines to an external display devicefor display to a patient or user. In other embodiments the output module604 of the system 600 is a display or other output device for displayingor otherwise outputting (for example via audio output) results of theclosed loop control simulation routines to the user.

In this manner, in aspects of the present disclosure it can be seen thatfor a diabetic person with poor blood glucose control, a set ofcontinuous blood glucose data collected over a long period of time mayinclude significant subsets of continuous data where the measured changein blood glucose is dominated by the influx of glucose from the gut andwhere insulin dependent utilization of glucose contributesinsignificantly. In these subsets of continuous data, blood glucoselevel rises at or near its maximum possible rate. Accordingly, in oneaspect, the one or more routines described herein links or associatesthose subsets of continuous data together with artificially constructedsubsets of blood glucose data that are extrapolations of the bloodglucose data that preceded them. This forms a hypothetical uncontrolledblood glucose excursion which can then be used as a tool in thedevelopment, testing and tuning of a closed loop blood glucose controlalgorithm.

A method for developing and testing a closed loop blood glucose controlalgorithms is disclosed. The method uses continuous blood glucose datato develop, test or tune a closed loop blood glucose control algorithm.The process takes a string of continuous blood glucose data andmathematically processes it to produce a hypothetical uncontrolled bloodglucose excursion. This uncontrolled blood glucose excursion can then beused as a test input to aid in developing, testing or tuning a closedloop blood glucose control algorithm. This method will allow a closedloop blood glucose control algorithm to be custom tailored to the uniquerequirements of an individual.

In the manner described above, in accordance with embodiments of thepresent disclosure, method for developing and testing a closed loopblood glucose control algorithm is provided. In one aspect, continuousblood glucose data may be used to generate a test input that can be usedas an aid in developing, testing or tuning a closed loop blood glucosecontrol algorithm. The routine may include a set of continuous bloodglucose data which is analyzed to generate or determine a hypotheticaluncontrolled blood glucose excursion. The uncontrolled blood glucoseexcursion may be used as a test input to aid in developing, testing ortuning a closed loop blood glucose control algorithm. In one aspect,this approach may allow a closed loop blood glucose control algorithm tobe custom tailored to the unique requirements of a diabetic individual.

In one embodiment, a method may include receiving glucose data for apredetermined time period, determining a variation in the glucose levelbased on the received glucose data, filtering the received glucose databased on the determined variation, substituting a negative change in theglucose data value with a predetermined value to generate a sequence ofmodified glucose values, and integrating the sequence of modifiedglucose values to determine an uncontrolled blood glucose excursioncondition.

In one aspect, the predetermined value may include an average value,where the average value may include an average of ten prior values.Alternatively, or in addition to, the average value may include aweighted average value, which may be an equally or unequally weightedaverage value.

In a further aspect, filtering based on the predetermined variation mayinclude filtering out glucose values associated with a negative change,where the negative change may be determined based on an immediate priorglucose value.

In another embodiment, an apparatus is disclosed which may include auser interface, one or more processors operatively coupled to the userinterface, and a memory for storing instructions which, when executed bythe one or more processors, causes the one or more processors to receiveglucose data for a predetermined time period, determine a variation inthe glucose level based on the received glucose data, filter thereceived glucose data based on the determined variation, substitute anegative change in the glucose data value with a predetermined value togenerate a sequence of modified glucose values, and integrating thesequence of modified glucose values to determine an uncontrolled bloodglucose excursion condition.

In another aspect, the memory for storing instructions which, whenexecuted by the one or more processors, may cause the one or moreprocessors to filter out glucose values associated with a negativechange, where the negative change may be determined based on animmediate prior glucose value.

In still another aspect, one or more storage devices having processorreadable code embodied thereon, said processor readable code forprogramming one or more processors to perform a control test algorithmmay comprise receiving glucose data for a predetermined time period,determining a variation in the glucose level based on the receivedglucose data, filtering the received glucose data based on thedetermined variation, substituting a negative change in the glucose datavalue with a predetermined value to generate a sequence of modifiedglucose values, and integrating the sequence of modified glucose valuesto determine an uncontrolled blood glucose excursion condition.

Various other modifications and alterations in the structure and methodof operation of this disclosure will be apparent to those skilled in theart without departing from the scope and spirit of the embodiments ofthe present disclosure. Although the present disclosure has beendescribed in connection with particular embodiments, it should beunderstood that the present disclosure as claimed should not be undulylimited to such particular embodiments. It is intended that thefollowing claims define the scope of the present disclosure and thatstructures and methods within the scope of these claims and theirequivalents be covered thereby.

What is claimed is:
 1. A method, comprising: receiving glucose data fora predetermined time period; determining a variation in the glucoselevel based on the received glucose data; filtering the received glucosedata based on the determined variation; substituting a negative changein the glucose data value with a predetermined value to generate asequence of modified glucose values; and integrating the sequence ofmodified glucose values to determine an uncontrolled blood glucoseexcursion condition.
 2. The method of claim 1 wherein the predeterminedvalue includes an average value.
 3. The method of claim 2 wherein theaverage value includes an average of ten prior values.
 4. The method ofclaim 2 wherein the average value includes a weighted average value. 5.The method of claim 4 wherein the weighed average value includes anequally weighted average value.
 6. The method of claim 4 wherein theweighed average value includes an unequally weighted average value. 7.The method of claim 2 wherein the average value includes an unweightedaverage value.
 8. The method of claim 1 wherein filtering based on thepredetermined variation includes filtering out glucose values associatedwith a negative change.
 9. The method of claim 8 wherein the negativechange is determined based on an immediate prior glucose value.
 10. Anapparatus, comprising: a user interface; one or more processorsoperatively coupled to the data user interface; and a memory for storinginstructions which, when executed by the one or more processors, causesthe one or more processors to receive glucose data for a predeterminedtime period, determine a variation in the glucose level based on thereceived glucose data, filter the received glucose data based on thedetermined variation, substitute a negative change in the glucose datavalue with a predetermined value to generate a sequence of modifiedglucose values, and integrating the sequence of modified glucose valuesto determine an uncontrolled blood glucose excursion condition.
 11. Theapparatus of claim 10 wherein the predetermined value includes anaverage value.
 12. The apparatus of claim 11 wherein the average valueincludes an average of ten prior values.
 13. The apparatus of claim 10wherein the average value includes a weighted average value.
 14. Theapparatus of claim 13 wherein the weighed average value includes anequally weighted average value.
 15. The apparatus of claim 13 whereinthe weighed average value includes an unequally weighted average value.16. The apparatus of claim 10 wherein the average value includes anunweighted average value.
 17. The apparatus of claim 10 wherein thememory for storing instructions which, when executed by the one or moreprocessors, causes the one or more processors to filter out glucosevalues associated with a negative change.
 18. The apparatus of claim 17wherein the negative change is determined based on an immediate priorglucose value.
 19. One or more storage devices having processor readablecode embodied thereon, said processor readable code for programming oneor more processors to perform a control test algorithm, comprising:receiving glucose data for a predetermined time period; determining avariation in the glucose level based on the received glucose data;filtering the received glucose data based on the determined variation;substituting a negative change in the glucose data value with apredetermined value to generate a sequence of modified glucose values;and integrating the sequence of modified glucose values to determine anuncontrolled blood glucose excursion condition.